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align docsum endpoint
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Signed-off-by: Xinyao Wang <[email protected]>
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XinyaoWa committed Jan 10, 2025
1 parent 0dd8a38 commit 26a0099
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Showing 10 changed files with 10 additions and 10 deletions.
2 changes: 1 addition & 1 deletion DocSum/docker_compose/amd/gpu/rocm/README.md
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Expand Up @@ -127,7 +127,7 @@ docker compose up -d
2. LLM Microservice

```bash
curl http://${host_ip}:9000/v1/chat/docsum \
curl http://${host_ip}:9000/v1/docsum \
-X POST \
-d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}' \
-H 'Content-Type: application/json'
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2 changes: 1 addition & 1 deletion DocSum/docker_compose/intel/cpu/xeon/README.md
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Expand Up @@ -140,7 +140,7 @@ You will have the following Docker Images:
2. LLM Microservice

```bash
curl http://${host_ip}:9000/v1/chat/docsum \
curl http://${host_ip}:9000/v1/docsum \
-X POST \
-d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}' \
-H 'Content-Type: application/json'
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2 changes: 1 addition & 1 deletion DocSum/docker_compose/intel/hpu/gaudi/README.md
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Expand Up @@ -132,7 +132,7 @@ You will have the following Docker Images:
2. LLM Microservice

```bash
curl http://${host_ip}:9000/v1/chat/docsum \
curl http://${host_ip}:9000/v1/docsum \
-X POST \
-d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}' \
-H 'Content-Type: application/json'
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2 changes: 1 addition & 1 deletion DocSum/docsum.py
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Expand Up @@ -146,7 +146,7 @@ def add_remote_service(self):
name="llm",
host=LLM_SERVICE_HOST_IP,
port=LLM_SERVICE_PORT,
endpoint="/v1/chat/docsum",
endpoint="/v1/docsum",
use_remote_service=True,
service_type=ServiceType.LLM,
)
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2 changes: 1 addition & 1 deletion DocSum/kubernetes/gmc/docsum_gaudi.yaml
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Expand Up @@ -23,7 +23,7 @@ spec:
internalService:
serviceName: docsum-llm-uservice
config:
endpoint: /v1/chat/docsum
endpoint: /v1/docsum
PORT: "9009"
TGI_LLM_ENDPOINT: tgi-gaudi-svc
- name: TgiGaudi
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2 changes: 1 addition & 1 deletion DocSum/kubernetes/gmc/docsum_xeon.yaml
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Expand Up @@ -23,7 +23,7 @@ spec:
internalService:
serviceName: docsum-llm-uservice
config:
endpoint: /v1/chat/docsum
endpoint: /v1/docsum
PORT: "9009"
TGI_LLM_ENDPOINT: tgi-svc
- name: Tgi
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2 changes: 1 addition & 1 deletion DocSum/tests/test_compose_on_gaudi.sh
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Expand Up @@ -157,7 +157,7 @@ function validate_microservices() {

# llm microservice
validate_services_json \
"${host_ip}:9000/v1/chat/docsum" \
"${host_ip}:9000/v1/docsum" \
"data: " \
"llm-docsum-tgi" \
"llm-docsum-gaudi-server" \
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2 changes: 1 addition & 1 deletion DocSum/tests/test_compose_on_rocm.sh
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Expand Up @@ -137,7 +137,7 @@ function validate_microservices() {

# llm microservice
validate_services \
"${host_ip}:9000/v1/chat/docsum" \
"${host_ip}:9000/v1/docsum" \
"data: " \
"docsum-llm-server" \
"docsum-llm-server" \
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2 changes: 1 addition & 1 deletion DocSum/tests/test_compose_on_xeon.sh
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Expand Up @@ -159,7 +159,7 @@ function validate_microservices() {

# llm microservice
validate_services_json \
"${host_ip}:9000/v1/chat/docsum" \
"${host_ip}:9000/v1/docsum" \
"data: " \
"llm-docsum-tgi" \
"llm-docsum-server" \
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2 changes: 1 addition & 1 deletion ProductivitySuite/docker_compose/intel/cpu/xeon/README.md
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Expand Up @@ -293,7 +293,7 @@ Please refer to **[keycloak_setup_guide](keycloak_setup_guide.md)** for more det
10. DocSum LLM Microservice

```bash
curl http://${host_ip}:9003/v1/chat/docsum\
curl http://${host_ip}:9003/v1/docsum\
-X POST \
-d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5"}' \
-H 'Content-Type: application/json'
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